Lung cancer remains the leading cause of mortality cancer. In 1999, there were approximately 170,000 new cases of lung cancer in the U.S., where approximately one in every eighteen women and approximately one in every twelve men develop lung cancer. Early detection of lung tumors (visible on chest film as pulmonary nodules) may increase the patient's chance of survival, but detecting pulmonary nodules is a complicated task. Nodules show up as relatively low-contrast white circular objects within the lung fields. The difficulty for computer aided image data search schemes is distinguishing true nodules from (overlapping) shadows, vessels and ribs.
The early stage detection of lung cancer remains an important goal in medical research. Regular chest radiography and sputum examination programs have proven ineffective in reducing mortality rates. Although screening for lung cancer with chest X-rays can detect early lung cancer, such screening can also possibly produce many false-positive test results, causing needless extra tests. Furthermore, while large (e.g., greater than 1 cm in diameter) malignant nodules are often relatively easy to detect with conventional screening equipment and can be diagnosed with needle biopsy or bronchoscopy techniques, these techniques are typically unsuitable for detecting smaller nodules, particularly if such nodules are located deep in the lung tissue or away from large airways. Thus, many of these techniques have been found to be unsuitable for early stage lung cancer detection.
At present, low-dose spiral computed tomography (LDCT) is of prime interest for screening (high risk) groups for early detection of lung cancer and is being studied by various groups, including the National Cancer Institute. LDCT provides chest scans with very high spatial, temporal, and contrast resolution of anatomic structures and is able to gather a complete 3D volume of a human thorax in a single breath-hold. Hence, for these reasons, in recent years most lung cancer screening programs are being investigated in the United States and Japan with LDCT as the screening modality of choice.
Automatic screening of image data from LDCT typically involves selecting initial candidate lung abnormalities (pulmonary nodules). Next, the false candidates, called false positive nodules (FPNs), are partially eliminated while preserving the true positive nodules (TPNs).
When selecting initial candidates, conformal nodule filtering or unsharp masking can enhance nodules and suppress other structures to separate the candidates from the background by simple thresholding or multiple gray-level thresholding techniques. A series of 3D cylindrical and spherical filters may be used to detect small lung nodules from high resolution CT images. Circular and semicircular nodule candidates may be detected by template matching. However, these spherical, cylindrical, or circular assumptions are typically not adequate for describing the general geometry of the lesions. This is because their shape can be irregular due to the spiculation or the attachments to the pleural surface (i.e., juxtapleural and peripheral) and vessels (i.e., vascularized). Morphological operators may be used to detect lung nodules. The drawbacks to these approaches are the difficulties in detecting lung wall nodules. Also, there are other pattern recognition techniques used in detection of lung nodules such as clustering, linear discriminant functions, rule-based classification, Hough transforms, connected component analysis of thresholded CT slices, gray level distance transforms, and patient-specific a priori models.
FPNs may be excluded by feature extraction and classification. Such features as circularity, size, contrast, or local curvature that are extracted by morphological techniques, or artificial neural networks (ANN), may be used as post-classifiers. Also, there are a number of classification techniques used in the final stage of some nodule detection systems to reduce the FPNs such as: rule-based or linear classifiers; template matching; nearest cluster; and Markov random field.
Two specific issues arising in connection with pulmonary nodule detection include image segmentation and registration. Image segmentation refers to the extraction of relevant image data from an image, e.g., in the context of pulmonary nodule detection, segmenting the lung tissues from an LDCT chest scan, or segmenting pulmonary nodules and/or candidates from surrounding lung tissues.
Image segmentation is under extensive study. Typical conventional techniques are based on fitting a Gaussian model to empirical data, but this approach becomes a challenge if initial measurements are corrupted with outliers and margin-truncation from neighboring structures. Some techniques to address segmentation include the use of anisotropic intensity model fitting with analytical parameter estimation. Other techniques segment 2D and 3D nodules based on thresholding voxel intensity; however, such attempts may be unreliable on cavity or non-solid nodules.
Therefore, a need exists in the art for an improved image segmentation technique for segmenting lung tissues from LDCT image scans.
Image registration refers to the alignment of multiple images for the purpose of detecting changes from one image to another. Within the context of pulmonary nodule detection, it has been found that one promising technique for detecting small cancerous nodules is based upon the growth rate of such nodules, as malignant nodules have been found to exhibit significantly higher rates of growth than benign nodules.
Image registration is an important step in tracking the growth of nodules over time, as growth is typically measured by comparing the size or volume of the nodules at two different points in time, i.e., by comparing the image data for the nodules taken at two points in time. Tracking growth from image data, however, can be a challenging task not only because of changes in a patient's position at each data acquisition, but also because of a patient's heartbeat and respiration during data acquisition. In order to accurately measure how nodules are developing over time, all of these motions need to be compensated for by registering LDCT data sets taken at different times.
Conventional approaches to register LDCT scans with one another typically exploit corresponding local structural elements or features in the images, e.g., using the centroids of local structures to apply rigid and affine image registration, using an objective function with an anisotropic smoothness constraint and a continuous mechanical model, using an optical flow and model based motion estimation technique, combining optical flow analysis with spirometric data in order to track breathing motion automatically, using a standard lung atlas to analyze pulmonary structures, exploiting landmark and intensity based registration algorithms to warp a template image to a lung volume, using an anisotropic intensity model fitting with analytical parameter estimation to evaluate nodule volume, or segmenting 2D and 3D nodules by thresholding voxel intensity followed by a connectivity filter.
Conventional approaches, however, have been found to inadequately account for large deformations of lung tissues due to heartbeat and respiration. Furthermore, these approaches have been found to not be suitable for some types of pulmonary nodules such as cavities and ground glass nodules, and many approaches require significant user interaction, which can be problematic in clinical settings.
Therefore, a need also exists in the art for an improved image registration technique in connection with monitoring the growth of pulmonary nodules from LDCT scans.